With the advent of shared content hosting platforms, a wide range of different types of multimedia, such as video content, image content, audio content, and so on, is finding its way to the Internet. In specific terms of the shared content hosting platform and online videos, the production quality of online video has a significant bearing on its potential to be monetized and the quality of user experience. Low quality videos often contain jarring camera motions, blocky pixel artifacts, out-of-focus picture, and poor lighting. Such videos negatively affect users' interest in consuming video content. As a result, there is value in identifying videos with good production quality and highlighting them on a shared content hosting platform.
Identifying a high production quality video is a challenging problem. There are many different metrics and heuristics that are correlated with video quality. However, no principled methods generally exist for learning how these different metrics and heuristics interact to predict the production quality of a video as perceived by a user.
There are three key problems generally encountered with previous work in this area. First, previous methods rely on access to a reference (usually an undistorted) image/video against which to compare and estimate image quality. However, for many media items, only one copy of the media item is available. Second, previous methods rely on raters providing absolute scores to an item in isolation from other items in the set. This can be unreliable because subjective judgment of quality is often relative and inconsistent. Third, previous methods generally study individual video quality features in isolation; they do not examine how the features combine to produce an overall measure of perceptual quality.